Comparative Study of Different Clustering Algorithms

Abstract

This paper presents a detailed study and comparison of different clustering based image segmentation algorithms. The traditional clustering algorithms are the hard clustering algorithm and the soft clustering algorithm. We have compared the hard k-means algorithm with the soft fuzzy c-means (FCM) algorithm. To overcome the limitations of conventional FCM we have also studied Kernel fuzzy c-means (KFCM) algorithm in detail. The K-means algorithm is sensitive to noise and outliers so, an extension of K-means called as Fuzzy c-means (FCM) are introduced. FCM allows data points to belong to more than one cluster where each data point has a degree of membership of belonging to each cluster. The KFCM uses a mapping function and gives better performance than FCM in case of noise corrupted images.